# Installing libraries
library(TAM) # for Rasch modeling
library(WrightMap) # to build item-person (Wright) maps
library(lsr) #to calculate Cohen's d
library(DescTools) #to calculate etasquared
library(Rmisc) # to calculate summary statistics
library(tidyverse)
library(cowplot)
library(knitr)
library(magick)
# Create logo banner for plots
earthlab_orig <- image_read(path = "earth-lab-logo-white.png") %>%
image_scale("x80")
twitter_orig <- image_read(path = "plot-footer-twitter.png") %>%
image_scale("x70")
black_banner <- image_read(path = "black-banner.png")
earthlab_logo <- image_composite(image_scale(black_banner, "1000x100"), earthlab_orig, offset = "+30+10")
twitter_logo <- image_composite(image_scale(black_banner, "1000x100"), twitter_orig, offset = "+540+15")
logo <- image_append(image_scale(c(earthlab_logo, twitter_logo)), stack = FALSE)
logo
Participant Demographics
A total of 53 consenting participants provided demographic information related to gender, race & ethnicity through a series of items included on the pre-program survey instrument, administered prior to the start of the technical workshops.
Raw Responses (%)
Python Skills
Data Science Skills
Data Science Communication
Data Science Practices
Science Identity
Rasch Modeling
Python Skills
Data Science Skills
Data Science Communication
Data Science Practices
Science Identity
Analysis of Variance
##
## Call:
## lm(formula = Ability ~ Trial + Dimension + Cohort + Cohort *
## Dimension, data = abil_trial_dimension_all6)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.228 -1.128 0.032 0.982 5.022
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 2.75115 0.32182 8.549
## TrialBefore -2.93001 0.16283 -17.994
## DimensionData Science Practices -0.05436 0.44032 -0.123
## DimensionData Science Skills 0.16079 0.44032 0.365
## DimensionPython Skills 2.11823 0.44032 4.811
## DimensionScience Identity -0.12235 0.44032 -0.278
## CohortYear 2 -0.09295 0.46503 -0.200
## CohortYear 3 -0.13972 0.42259 -0.331
## DimensionData Science Practices:CohortYear 2 -0.24793 0.65766 -0.377
## DimensionData Science Skills:CohortYear 2 0.32802 0.66517 0.493
## DimensionPython Skills:CohortYear 2 0.72306 0.66517 1.087
## DimensionScience Identity:CohortYear 2 -0.59235 0.66517 -0.891
## DimensionData Science Practices:CohortYear 3 -0.12938 0.59763 -0.216
## DimensionData Science Skills:CohortYear 3 0.12335 0.60141 0.205
## DimensionPython Skills:CohortYear 3 0.70361 0.60141 1.170
## DimensionScience Identity:CohortYear 3 -0.62037 0.60141 -1.032
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## TrialBefore < 2e-16 ***
## DimensionData Science Practices 0.902
## DimensionData Science Skills 0.715
## DimensionPython Skills 2.05e-06 ***
## DimensionScience Identity 0.781
## CohortYear 2 0.842
## CohortYear 3 0.741
## DimensionData Science Practices:CohortYear 2 0.706
## DimensionData Science Skills:CohortYear 2 0.622
## DimensionPython Skills:CohortYear 2 0.278
## DimensionScience Identity:CohortYear 2 0.374
## DimensionData Science Practices:CohortYear 3 0.829
## DimensionData Science Skills:CohortYear 3 0.838
## DimensionPython Skills:CohortYear 3 0.243
## DimensionScience Identity:CohortYear 3 0.303
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.761 on 452 degrees of freedom
## Multiple R-squared: 0.5314, Adjusted R-squared: 0.5158
## F-statistic: 34.17 on 15 and 452 DF, p-value: < 2.2e-16## Analysis of Variance Table
##
## Response: Ability
## Df Sum Sq Mean Sq F value Pr(>F)
## Trial 1 1004.44 1004.44 323.7867 <2e-16 ***
## Dimension 4 563.71 140.93 45.4291 <2e-16 ***
## Cohort 2 1.34 0.67 0.2158 0.8060
## Dimension:Cohort 8 20.59 2.57 0.8295 0.5769
## Residuals 452 1402.18 3.10
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## eta.sq eta.sq.part
## Trial 0.3356796615 0.4173656492
## Dimension 0.1883110526 0.2866604141
## Cohort 0.0004473933 0.0009538292
## Dimension:Cohort 0.0068797627 0.0144690270t-testing
Data Science Communication
##
## Welch Two Sample t-test
##
## data: comfortpost$Ability and comfortpre$Ability
## t = 10.27, df = 87.882, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.950954 2.887157
## sample estimates:
## mean of x mean of y
## 2.415192354 -0.003863267## [1] 2.096379Data Science Communication by Year
# science identity pre by year
ds_comms_pre1 <- filter(comfortpre, Cohort == "Year 1")
ds_comms_pre1## Ability Dimension Cohort Trial
## 1 0.31876992 Data Science Communication Year 1 Before
## 2 -0.50742352 Data Science Communication Year 1 Before
## 3 1.77450789 Data Science Communication Year 1 Before
## 4 -0.37201079 Data Science Communication Year 1 Before
## 5 0.90986431 Data Science Communication Year 1 Before
## 6 0.60796709 Data Science Communication Year 1 Before
## 7 -0.50742352 Data Science Communication Year 1 Before
## 8 0.03831258 Data Science Communication Year 1 Before
## 9 0.03831258 Data Science Communication Year 1 Before
## 10 -0.09951847 Data Science Communication Year 1 Before
## 11 0.17763546 Data Science Communication Year 1 Before
## 12 2.46706048 Data Science Communication Year 1 Before
## 13 0.31876992 Data Science Communication Year 1 Before
## 14 0.46207106 Data Science Communication Year 1 Before
## 15 0.90986431 Data Science Communication Year 1 Before
## 16 0.75699922 Data Science Communication Year 1 Beforeds_comms_pre2 <- filter(comfortpre, Cohort == "Year 2")
ds_comms_pre2## Ability Dimension Cohort Trial
## 1 0.03831258 Data Science Communication Year 2 Before
## 2 -0.50742352 Data Science Communication Year 2 Before
## 3 -1.65983969 Data Science Communication Year 2 Before
## 4 -1.65983969 Data Science Communication Year 2 Before
## 5 0.17763546 Data Science Communication Year 2 Before
## 6 0.90986431 Data Science Communication Year 2 Before
## 7 -0.23617675 Data Science Communication Year 2 Before
## 8 -1.34572147 Data Science Communication Year 2 Before
## 9 -0.50742352 Data Science Communication Year 2 Before
## 10 -0.23617675 Data Science Communication Year 2 Before
## 11 0.03831258 Data Science Communication Year 2 Before
## 12 1.58218261 Data Science Communication Year 2 Before
## 13 -0.50742352 Data Science Communication Year 2 Beforeds_comms_pre3 <- filter(comfortpre, Cohort == "Year 3")
ds_comms_pre3## Ability Dimension Cohort Trial
## 1 -0.23617675 Data Science Communication Year 3 Before
## 2 0.03831258 Data Science Communication Year 3 Before
## 3 1.58218261 Data Science Communication Year 3 Before
## 4 -1.34572147 Data Science Communication Year 3 Before
## 5 0.03831258 Data Science Communication Year 3 Before
## 6 0.03831258 Data Science Communication Year 3 Before
## 7 0.46207106 Data Science Communication Year 3 Before
## 8 -0.09951847 Data Science Communication Year 3 Before
## 9 -0.23617675 Data Science Communication Year 3 Before
## 10 -0.50742352 Data Science Communication Year 3 Before
## 11 0.46207106 Data Science Communication Year 3 Before
## 12 0.17763546 Data Science Communication Year 3 Before
## 13 -0.37201079 Data Science Communication Year 3 Before
## 14 -1.19864112 Data Science Communication Year 3 Before
## 15 2.21085346 Data Science Communication Year 3 Before
## 16 -2.74709468 Data Science Communication Year 3 Before
## 17 -1.49876757 Data Science Communication Year 3 Before
## 18 -0.37201079 Data Science Communication Year 3 Before
## 19 0.03831258 Data Science Communication Year 3 Before# science identity post by year
ds_comms_post1 <- filter(comfortpost, Cohort == "Year 1")
ds_comms_post1## Ability Dimension Cohort Trial
## 1 1.7657308 Data Science Communication Year 1 After
## 2 1.7657308 Data Science Communication Year 1 After
## 3 3.8788828 Data Science Communication Year 1 After
## 4 1.0660680 Data Science Communication Year 1 After
## 5 3.0255702 Data Science Communication Year 1 After
## 6 2.1895153 Data Science Communication Year 1 After
## 7 0.7564803 Data Science Communication Year 1 After
## 8 2.4334785 Data Science Communication Year 1 After
## 9 1.3983095 Data Science Communication Year 1 After
## 10 1.5765678 Data Science Communication Year 1 After
## 11 1.3983095 Data Science Communication Year 1 After
## 12 3.0255702 Data Science Communication Year 1 After
## 13 1.5765678 Data Science Communication Year 1 After
## 14 1.5765678 Data Science Communication Year 1 After
## 15 3.4039561 Data Science Communication Year 1 After
## 16 3.0255702 Data Science Communication Year 1 Afterds_comms_post2 <- filter(comfortpost, Cohort == "Year 2")
ds_comms_post2## Ability Dimension Cohort Trial
## 1 1.9687354 Data Science Communication Year 2 After
## 2 2.7083229 Data Science Communication Year 2 After
## 3 3.4039561 Data Science Communication Year 2 After
## 4 4.5387378 Data Science Communication Year 2 After
## 5 1.5765678 Data Science Communication Year 2 After
## 6 1.5765678 Data Science Communication Year 2 After
## 7 1.3983095 Data Science Communication Year 2 After
## 8 2.1895153 Data Science Communication Year 2 After
## 9 1.9687354 Data Science Communication Year 2 After
## 10 0.9090075 Data Science Communication Year 2 After
## 11 3.8788828 Data Science Communication Year 2 After
## 12 5.7939364 Data Science Communication Year 2 After
## 13 3.0255702 Data Science Communication Year 2 Afterds_comms_post3 <- filter(comfortpost, Cohort == "Year 3")
ds_comms_post3## Ability Dimension Cohort Trial
## 1 2.4334785 Data Science Communication Year 3 After
## 2 1.5765678 Data Science Communication Year 3 After
## 3 5.7939364 Data Science Communication Year 3 After
## 4 1.9687354 Data Science Communication Year 3 After
## 5 1.9687354 Data Science Communication Year 3 After
## 6 1.7657308 Data Science Communication Year 3 After
## 7 1.2287191 Data Science Communication Year 3 After
## 8 2.1895153 Data Science Communication Year 3 After
## 9 0.7564803 Data Science Communication Year 3 After
## 10 5.7939364 Data Science Communication Year 3 After
## 11 1.2287191 Data Science Communication Year 3 After
## 12 1.5765678 Data Science Communication Year 3 After
## 13 3.8788828 Data Science Communication Year 3 After
## 14 0.4618885 Data Science Communication Year 3 After
## 15 3.8788828 Data Science Communication Year 3 After
## 16 3.4039561 Data Science Communication Year 3 After
## 17 1.7657308 Data Science Communication Year 3 After
## 18 2.4334785 Data Science Communication Year 3 After
## 19 3.0255702 Data Science Communication Year 3 After# t-testing data science practices by year
# Year 1
t.test(ds_comms_post1$Ability, ds_comms_pre1$Ability)##
## Welch Two Sample t-test
##
## data: ds_comms_post1$Ability and ds_comms_pre1$Ability
## t = 5.4751, df = 29.515, p-value = 6.412e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.040732 2.280408
## sample estimates:
## mean of x mean of y
## 2.1164297 0.4558599cohensD(ds_comms_post1$Ability, ds_comms_pre1$Ability)## [1] 1.93574# Year 2
t.test(ds_comms_post2$Ability, ds_comms_pre2$Ability)##
## Welch Two Sample t-test
##
## data: ds_comms_post2$Ability and ds_comms_pre2$Ability
## t = 6.3928, df = 20.827, p-value = 2.549e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 2.015839 3.961171
## sample estimates:
## mean of x mean of y
## 2.6874496 -0.3010552cohensD(ds_comms_post2$Ability, ds_comms_pre2$Ability)## [1] 2.507472# Year 3
t.test(ds_comms_post3$Ability, ds_comms_pre3$Ability)##
## Welch Two Sample t-test
##
## data: ds_comms_post3$Ability and ds_comms_pre3$Ability
## t = 6.2996, df = 32.67, p-value = 4.182e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.806121 3.530194
## sample estimates:
## mean of x mean of y
## 2.4805006 -0.1876567cohensD(ds_comms_post3$Ability, ds_comms_pre3$Ability)## [1] 2.043861Data Science Practices
##
## Welch Two Sample t-test
##
## data: confpost$Ability and confpre$Ability
## t = 7.5006, df = 92.239, p-value = 3.837e-11
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.531676 2.634901
## sample estimates:
## mean of x mean of y
## 2.074584526 -0.008704105## [1] 1.531062Data Science Practices by Year
# science identity pre by year
ds_pract_pre1 <- filter(confpre, Cohort == "Year 1")
ds_pract_pre1## Ability Dimension Cohort Trial
## 1 3.01420897 Data Science Practices Year 1 Before
## 2 0.13092222 Data Science Practices Year 1 Before
## 3 0.73619507 Data Science Practices Year 1 Before
## 4 -0.06921477 Data Science Practices Year 1 Before
## 5 1.59756681 Data Science Practices Year 1 Before
## 6 1.15428309 Data Science Practices Year 1 Before
## 7 -1.56252692 Data Science Practices Year 1 Before
## 8 -1.32768269 Data Science Practices Year 1 Before
## 9 -0.88813267 Data Science Practices Year 1 Before
## 10 0.73619507 Data Science Practices Year 1 Before
## 11 -0.47277589 Data Science Practices Year 1 Before
## 12 0.94296217 Data Science Practices Year 1 Before
## 13 0.94296217 Data Science Practices Year 1 Before
## 14 1.83444032 Data Science Practices Year 1 Before
## 15 0.33123446 Data Science Practices Year 1 Before
## 16 1.15428309 Data Science Practices Year 1 Beforeds_pract_pre2 <- filter(confpre, Cohort == "Year 2")
ds_pract_pre2## Ability Dimension Cohort Trial
## 1 -0.88813267 Data Science Practices Year 2 Before
## 2 -0.06921477 Data Science Practices Year 2 Before
## 3 -0.88813267 Data Science Practices Year 2 Before
## 4 -2.38567830 Data Science Practices Year 2 Before
## 5 0.73619507 Data Science Practices Year 2 Before
## 6 -0.27011420 Data Science Practices Year 2 Before
## 7 1.59756681 Data Science Practices Year 2 Before
## 8 -3.77058166 Data Science Practices Year 2 Before
## 9 -0.67833205 Data Science Practices Year 2 Before
## 10 -0.27011420 Data Science Practices Year 2 Before
## 11 0.33123446 Data Science Practices Year 2 Before
## 12 0.13092222 Data Science Practices Year 2 Before
## 13 -1.56252692 Data Science Practices Year 2 Beforeds_pract_pre3 <- filter(confpre, Cohort == "Year 3")
ds_pract_pre3## Ability Dimension Cohort Trial
## 1 1.1542831 Data Science Practices Year 3 Before
## 2 -0.6783320 Data Science Practices Year 3 Before
## 3 1.1542831 Data Science Practices Year 3 Before
## 4 -2.0836556 Data Science Practices Year 3 Before
## 5 0.3312345 Data Science Practices Year 3 Before
## 6 0.1309222 Data Science Practices Year 3 Before
## 7 1.1542831 Data Science Practices Year 3 Before
## 8 0.3312345 Data Science Practices Year 3 Before
## 9 -0.2701142 Data Science Practices Year 3 Before
## 10 0.5326596 Data Science Practices Year 3 Before
## 11 -0.2701142 Data Science Practices Year 3 Before
## 12 1.1542831 Data Science Practices Year 3 Before
## 13 2.6630033 Data Science Practices Year 3 Before
## 14 -0.8881327 Data Science Practices Year 3 Before
## 15 2.6630033 Data Science Practices Year 3 Before
## 16 -3.7705817 Data Science Practices Year 3 Before
## 17 -1.3276827 Data Science Practices Year 3 Before
## 18 -1.1038583 Data Science Practices Year 3 Before
## 19 -1.5625269 Data Science Practices Year 3 Before# science identity post by year
ds_pract_post1 <- filter(confpost, Cohort == "Year 1")
ds_pract_post1## Ability Dimension Cohort Trial
## 1 3.44744183 Data Science Practices Year 1 After
## 2 0.94296217 Data Science Practices Year 1 After
## 3 2.66300328 Data Science Practices Year 1 After
## 4 1.15428309 Data Science Practices Year 1 After
## 5 3.01420897 Data Science Practices Year 1 After
## 6 2.08638951 Data Science Practices Year 1 After
## 7 0.33123446 Data Science Practices Year 1 After
## 8 3.44744183 Data Science Practices Year 1 After
## 9 0.53265958 Data Science Practices Year 1 After
## 10 1.15428309 Data Science Practices Year 1 After
## 11 1.59756681 Data Science Practices Year 1 After
## 12 2.66300328 Data Science Practices Year 1 After
## 13 -0.06921477 Data Science Practices Year 1 After
## 14 2.08638951 Data Science Practices Year 1 After
## 15 2.66300328 Data Science Practices Year 1 After
## 16 3.44744183 Data Science Practices Year 1 Afterds_pract_post2 <- filter(confpost, Cohort == "Year 2")
ds_pract_post2## Ability Dimension Cohort Trial
## 1 1.1542831 Data Science Practices Year 2 After
## 2 2.6630033 Data Science Practices Year 2 After
## 3 3.4474418 Data Science Practices Year 2 After
## 4 3.4474418 Data Science Practices Year 2 After
## 5 2.6630033 Data Science Practices Year 2 After
## 6 1.1542831 Data Science Practices Year 2 After
## 7 2.6630033 Data Science Practices Year 2 After
## 8 2.3593798 Data Science Practices Year 2 After
## 9 1.5975668 Data Science Practices Year 2 After
## 10 1.1542831 Data Science Practices Year 2 After
## 11 2.6630033 Data Science Practices Year 2 After
## 12 5.2408556 Data Science Practices Year 2 After
## 13 0.9429622 Data Science Practices Year 2 Afterds_pract_post3 <- filter(confpost, Cohort == "Year 3")
ds_pract_post3## Ability Dimension Cohort Trial
## 1 3.4474418 Data Science Practices Year 3 After
## 2 1.1542831 Data Science Practices Year 3 After
## 3 5.2408556 Data Science Practices Year 3 After
## 4 1.1542831 Data Science Practices Year 3 After
## 5 1.3717866 Data Science Practices Year 3 After
## 6 3.0142090 Data Science Practices Year 3 After
## 7 1.1542831 Data Science Practices Year 3 After
## 8 2.0863895 Data Science Practices Year 3 After
## 9 0.5326596 Data Science Practices Year 3 After
## 10 3.0142090 Data Science Practices Year 3 After
## 11 0.7361951 Data Science Practices Year 3 After
## 12 2.6630033 Data Science Practices Year 3 After
## 13 -0.6783320 Data Science Practices Year 3 After
## 14 1.1542831 Data Science Practices Year 3 After
## 15 3.4474418 Data Science Practices Year 3 After
## 16 3.0142090 Data Science Practices Year 3 After
## 17 1.1542831 Data Science Practices Year 3 After
## 18 0.9429622 Data Science Practices Year 3 After
## 19 2.6630033 Data Science Practices Year 3 After# t-testing data science practices by year
# Year 1
t.test(ds_pract_post1$Ability, ds_pract_pre1$Ability)##
## Welch Two Sample t-test
##
## data: ds_pract_post1$Ability and ds_pract_pre1$Ability
## t = 3.4162, df = 29.98, p-value = 0.001844
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.5757861 2.2876111
## sample estimates:
## mean of x mean of y
## 1.9476311 0.5159325cohensD(ds_pract_post1$Ability, ds_pract_pre1$Ability)## [1] 1.207822# Year 2
t.test(ds_pract_post2$Ability, ds_pract_pre2$Ability)##
## Welch Two Sample t-test
##
## data: ds_pract_post2$Ability and ds_pract_pre2$Ability
## t = 5.8855, df = 23.661, p-value = 4.777e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.954043 4.067099
## sample estimates:
## mean of x mean of y
## 2.3961931 -0.6143776cohensD(ds_pract_post2$Ability, ds_pract_pre2$Ability)## [1] 2.308496# Year 3
t.test(ds_pract_post3$Ability, ds_pract_pre3$Ability)##
## Welch Two Sample t-test
##
## data: ds_pract_post3$Ability and ds_pract_pre3$Ability
## t = 4.1292, df = 35.364, p-value = 0.0002114
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.015828 2.979252
## sample estimates:
## mean of x mean of y
## 1.96144469 -0.03609519cohensD(ds_pract_post3$Ability, ds_pract_pre3$Ability)## [1] 1.339705Science Identity
##
## Welch Two Sample t-test
##
## data: idpost$Ability and idpre$Ability
## t = 3.3174, df = 82.505, p-value = 0.001353
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.6008267 2.4004040
## sample estimates:
## mean of x mean of y
## 1.43789668 -0.06271865## [1] 0.6917187Science Identity by Year
# science identity pre by year
idpre1 <- filter(idpre, Cohort == "Year 1")
idpre1## Ability Dimension Cohort Trial
## 1 1.8153924 Science Identity Year 1 Before
## 2 -1.2941506 Science Identity Year 1 Before
## 3 3.9602714 Science Identity Year 1 Before
## 4 0.4735946 Science Identity Year 1 Before
## 5 1.4568030 Science Identity Year 1 Before
## 6 1.1273724 Science Identity Year 1 Before
## 7 1.1273724 Science Identity Year 1 Before
## 8 -0.7836363 Science Identity Year 1 Before
## 9 -1.2941506 Science Identity Year 1 Before
## 10 2.2396942 Science Identity Year 1 Before
## 11 0.8055143 Science Identity Year 1 Before
## 12 1.8153924 Science Identity Year 1 Before
## 13 0.4735946 Science Identity Year 1 Before
## 14 0.4735946 Science Identity Year 1 Before
## 15 -0.3015740 Science Identity Year 1 Before
## 16 0.4735946 Science Identity Year 1 Beforeidpre2 <- filter(idpre, Cohort == "Year 2")
idpre2## Ability Dimension Cohort Trial
## 1 -0.7836363 Science Identity Year 2 Before
## 2 2.2396942 Science Identity Year 2 Before
## 3 -2.5847635 Science Identity Year 2 Before
## 4 -4.2749159 Science Identity Year 2 Before
## 5 -2.9679977 Science Identity Year 2 Before
## 6 -2.9679977 Science Identity Year 2 Before
## 7 -0.7836363 Science Identity Year 2 Before
## 8 -0.3015740 Science Identity Year 2 Before
## 9 -3.7820238 Science Identity Year 2 Before
## 10 3.9602714 Science Identity Year 2 Before
## 11 -0.7836363 Science Identity Year 2 Before
## 12 3.9602714 Science Identity Year 2 Beforeidpre3 <- filter(idpre, Cohort == "Year 3")
idpre3## Ability Dimension Cohort Trial
## 1 -0.7836363 Science Identity Year 3 Before
## 2 -0.7836363 Science Identity Year 3 Before
## 3 -0.7836363 Science Identity Year 3 Before
## 4 1.1273724 Science Identity Year 3 Before
## 5 0.8055143 Science Identity Year 3 Before
## 6 -1.7665849 Science Identity Year 3 Before
## 7 -3.7820238 Science Identity Year 3 Before
## 8 -3.3593292 Science Identity Year 3 Before
## 9 -2.1907468 Science Identity Year 3 Before
## 10 3.9602714 Science Identity Year 3 Before
## 11 -6.2108718 Science Identity Year 3 Before
## 12 2.8183989 Science Identity Year 3 Before
## 13 2.2396942 Science Identity Year 3 Before
## 14 0.1124107 Science Identity Year 3 Before
## 15 -3.7820238 Science Identity Year 3 Before
## 16 2.8183989 Science Identity Year 3 Before
## 17 3.9602714 Science Identity Year 3 Before
## 18 -0.7836363 Science Identity Year 3 Before# science identity post by year
idpost1 <- filter(idpost, Cohort == "Year 1")
idpost1## Ability Dimension Cohort Trial
## 1 2.2396950 Science Identity Year 1 After
## 2 -0.3015739 Science Identity Year 1 After
## 3 3.9602714 Science Identity Year 1 After
## 4 0.4735945 Science Identity Year 1 After
## 5 2.8183984 Science Identity Year 1 After
## 6 1.8153930 Science Identity Year 1 After
## 7 2.8183984 Science Identity Year 1 After
## 8 2.8183984 Science Identity Year 1 After
## 9 -0.7836289 Science Identity Year 1 After
## 10 2.2396950 Science Identity Year 1 After
## 11 0.8055138 Science Identity Year 1 After
## 12 2.2396950 Science Identity Year 1 After
## 13 0.1124107 Science Identity Year 1 After
## 14 0.4735945 Science Identity Year 1 After
## 15 1.1273719 Science Identity Year 1 After
## 16 1.8153930 Science Identity Year 1 Afteridpost2 <- filter(idpost, Cohort == "Year 2")
idpost2## Ability Dimension Cohort Trial
## 1 1.4568030 Science Identity Year 2 After
## 2 3.9602714 Science Identity Year 2 After
## 3 2.2396950 Science Identity Year 2 After
## 4 -1.2941469 Science Identity Year 2 After
## 5 -1.7665849 Science Identity Year 2 After
## 6 -0.7836289 Science Identity Year 2 After
## 7 3.9602714 Science Identity Year 2 After
## 8 1.8153930 Science Identity Year 2 After
## 9 2.2396950 Science Identity Year 2 After
## 10 3.9602714 Science Identity Year 2 After
## 11 0.8055138 Science Identity Year 2 After
## 12 3.9602714 Science Identity Year 2 Afteridpost3 <- filter(idpost, Cohort == "Year 3")
idpost3## Ability Dimension Cohort Trial
## 1 0.1124107 Science Identity Year 3 After
## 2 -0.7836289 Science Identity Year 3 After
## 3 -0.3015739 Science Identity Year 3 After
## 4 3.9602714 Science Identity Year 3 After
## 5 2.2396950 Science Identity Year 3 After
## 6 -1.7665849 Science Identity Year 3 After
## 7 -3.3593342 Science Identity Year 3 After
## 8 1.1273719 Science Identity Year 3 After
## 9 -0.3015739 Science Identity Year 3 After
## 10 3.9602714 Science Identity Year 3 After
## 11 1.4568030 Science Identity Year 3 After
## 12 2.8183984 Science Identity Year 3 After
## 13 2.2396950 Science Identity Year 3 After
## 14 0.4735945 Science Identity Year 3 After
## 15 0.8055138 Science Identity Year 3 After
## 16 2.8183984 Science Identity Year 3 After
## 17 3.9602714 Science Identity Year 3 After
## 18 1.4568030 Science Identity Year 3 After# t-testing science identity by year
# Year 1
t.test(idpost1$Ability, idpre1$Ability)##
## Welch Two Sample t-test
##
## data: idpost1$Ability and idpre1$Ability
## t = 1.5967, df = 29.977, p-value = 0.1208
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2111635 1.7241560
## sample estimates:
## mean of x mean of y
## 1.5420388 0.7855425cohensD(idpost1$Ability, idpre1$Ability)## [1] 0.5645036# Year 2
t.test(idpost2$Ability, idpre2$Ability)##
## Welch Two Sample t-test
##
## data: idpost2$Ability and idpre2$Ability
## t = 2.4198, df = 20.336, p-value = 0.025
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.3428631 4.5944318
## sample estimates:
## mean of x mean of y
## 1.7128187 -0.7558287cohensD(idpost2$Ability, idpre2$Ability)## [1] 0.9878965# Year 3
t.test(idpost3$Ability, idpre3$Ability)##
## Welch Two Sample t-test
##
## data: idpost3$Ability and idpre3$Ability
## t = 1.8263, df = 30.586, p-value = 0.07758
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1780179 3.2114174
## sample estimates:
## mean of x mean of y
## 1.1620446 -0.3546552cohensD(idpost3$Ability, idpre3$Ability)## [1] 0.60876# Year 3 vs. 1
t.test(idpost3$Ability, idpost1$Ability)##
## Welch Two Sample t-test
##
## data: idpost3$Ability and idpost1$Ability
## t = -0.65286, df = 29.458, p-value = 0.5189
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.5696000 0.8096115
## sample estimates:
## mean of x mean of y
## 1.162045 1.542039cohensD(idpost3$Ability, idpre1$Ability)## [1] 0.2151985# Looking at differences in growth across the three cohorts
sci_id_growth <- read.csv("sci-id-growth.csv")
sci_id_growth## Cohort Diff
## 1 Year 1 0.424302606
## 2 Year 1 0.992576732
## 3 Year 1 -0.000000036
## 4 Year 1 -0.000000144
## 5 Year 1 1.361595374
## 6 Year 1 0.688020626
## 7 Year 1 1.691026016
## 8 Year 1 3.602034707
## 9 Year 1 0.510521677
## 10 Year 1 0.000000788
## 11 Year 1 -0.000000502
## 12 Year 1 0.424302606
## 13 Year 1 -0.361183961
## 14 Year 1 -0.000000144
## 15 Year 1 1.428945868
## 16 Year 1 1.341798371
## 17 Year 2 2.240439321
## 18 Year 2 1.720577168
## 19 Year 2 4.824458528
## 20 Year 2 2.980769085
## 21 Year 2 1.201412741
## 22 Year 2 2.184368750
## 23 Year 2 4.743907684
## 24 Year 2 2.116966986
## 25 Year 2 6.021718816
## 26 Year 2 -0.000000036
## 27 Year 2 1.589150093
## 28 Year 2 -0.000000036
## 29 Year 3 0.896046985
## 30 Year 3 0.000007380
## 31 Year 3 0.482062438
## 32 Year 3 2.832898993
## 33 Year 3 1.434180709
## 34 Year 3 0.000000001
## 35 Year 3 0.422689603
## 36 Year 3 4.486701108
## 37 Year 3 1.889172951
## 38 Year 3 -0.000000036
## 39 Year 3 7.667674865
## 40 Year 3 -0.000000490
## 41 Year 3 0.000000788
## 42 Year 3 0.361183817
## 43 Year 3 4.587537605
## 44 Year 3 -0.000000490
## 45 Year 3 -0.000000036
## 46 Year 3 2.240439321sci_id_growth1 <- filter(sci_id_growth, Cohort == "Year 1")
sci_id_growth1## Cohort Diff
## 1 Year 1 0.424302606
## 2 Year 1 0.992576732
## 3 Year 1 -0.000000036
## 4 Year 1 -0.000000144
## 5 Year 1 1.361595374
## 6 Year 1 0.688020626
## 7 Year 1 1.691026016
## 8 Year 1 3.602034707
## 9 Year 1 0.510521677
## 10 Year 1 0.000000788
## 11 Year 1 -0.000000502
## 12 Year 1 0.424302606
## 13 Year 1 -0.361183961
## 14 Year 1 -0.000000144
## 15 Year 1 1.428945868
## 16 Year 1 1.341798371sci_id_growth2 <- filter(sci_id_growth, Cohort == "Year 2")
sci_id_growth2## Cohort Diff
## 1 Year 2 2.240439321
## 2 Year 2 1.720577168
## 3 Year 2 4.824458528
## 4 Year 2 2.980769085
## 5 Year 2 1.201412741
## 6 Year 2 2.184368750
## 7 Year 2 4.743907684
## 8 Year 2 2.116966986
## 9 Year 2 6.021718816
## 10 Year 2 -0.000000036
## 11 Year 2 1.589150093
## 12 Year 2 -0.000000036sci_id_growth3 <- filter(sci_id_growth, Cohort == "Year 3")
sci_id_growth3## Cohort Diff
## 1 Year 3 0.896046985
## 2 Year 3 0.000007380
## 3 Year 3 0.482062438
## 4 Year 3 2.832898993
## 5 Year 3 1.434180709
## 6 Year 3 0.000000001
## 7 Year 3 0.422689603
## 8 Year 3 4.486701108
## 9 Year 3 1.889172951
## 10 Year 3 -0.000000036
## 11 Year 3 7.667674865
## 12 Year 3 -0.000000490
## 13 Year 3 0.000000788
## 14 Year 3 0.361183817
## 15 Year 3 4.587537605
## 16 Year 3 -0.000000490
## 17 Year 3 -0.000000036
## 18 Year 3 2.240439321# t-testing for differences in growth
# Year 2 vs. Year 1
t.test(sci_id_growth2$Diff, sci_id_growth1$Diff)##
## Welch Two Sample t-test
##
## data: sci_id_growth2$Diff and sci_id_growth1$Diff
## t = 2.8688, df = 15.509, p-value = 0.01141
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.4436788 2.9806235
## sample estimates:
## mean of x mean of y
## 2.4686474 0.7564963# Year 3 vs. Year 1
t.test(sci_id_growth3$Diff, sci_id_growth1$Diff)##
## Welch Two Sample t-test
##
## data: sci_id_growth3$Diff and sci_id_growth1$Diff
## t = 1.349, df = 24.446, p-value = 0.1897
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.401722 1.922129
## sample estimates:
## mean of x mean of y
## 1.5166998 0.7564963cohensD(sci_id_growth3$Diff, sci_id_growth1$Diff)## [1] 0.4455407Data Science Skills
##
## Welch Two Sample t-test
##
## data: techconfpost$Ability and techconfpre$Ability
## t = 10.45, df = 89.127, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 2.431845 3.573682
## sample estimates:
## mean of x mean of y
## 3.0032369769 0.0004732148## [1] 2.179052Data Science Skills by Year
# science identity pre by year
ds_skills_pre1 <- filter(techconfpre, Cohort == "Year 1")
ds_skills_pre1## Ability Dimension Cohort Trial
## 1 1.78498502 Data Science Skills Year 1 Before
## 2 -2.89555118 Data Science Skills Year 1 Before
## 3 0.76338580 Data Science Skills Year 1 Before
## 4 0.19183423 Data Science Skills Year 1 Before
## 5 0.39200645 Data Science Skills Year 1 Before
## 6 -0.51889635 Data Science Skills Year 1 Before
## 7 0.58165000 Data Science Skills Year 1 Before
## 8 3.40743841 Data Science Skills Year 1 Before
## 9 0.39200645 Data Science Skills Year 1 Before
## 10 -0.02249625 Data Science Skills Year 1 Before
## 11 -0.25643576 Data Science Skills Year 1 Before
## 12 0.39200645 Data Science Skills Year 1 Before
## 13 -0.51889635 Data Science Skills Year 1 Before
## 14 0.39200645 Data Science Skills Year 1 Before
## 15 -0.02249625 Data Science Skills Year 1 Before
## 16 -0.82595605 Data Science Skills Year 1 Beforeds_skills_pre2 <- filter(techconfpre, Cohort == "Year 2")
ds_skills_pre2## Ability Dimension Cohort Trial
## 1 0.5816500 Data Science Skills Year 2 Before
## 2 1.7849850 Data Science Skills Year 2 Before
## 3 -2.8955512 Data Science Skills Year 2 Before
## 4 -0.2564358 Data Science Skills Year 2 Before
## 5 1.7849850 Data Science Skills Year 2 Before
## 6 1.6156635 Data Science Skills Year 2 Before
## 7 0.1918342 Data Science Skills Year 2 Before
## 8 0.3920065 Data Science Skills Year 2 Before
## 9 1.1109643 Data Science Skills Year 2 Before
## 10 -0.8259560 Data Science Skills Year 2 Before
## 11 -1.7612494 Data Science Skills Year 2 Before
## 12 -0.8259560 Data Science Skills Year 2 Beforeds_skills_pre3 <- filter(techconfpre, Cohort == "Year 3")
ds_skills_pre3## Ability Dimension Cohort Trial
## 1 0.7633858 Data Science Skills Year 3 Before
## 2 -2.8955512 Data Science Skills Year 3 Before
## 3 -0.5188964 Data Science Skills Year 3 Before
## 4 2.5077364 Data Science Skills Year 3 Before
## 5 -1.7612494 Data Science Skills Year 3 Before
## 6 0.7633858 Data Science Skills Year 3 Before
## 7 -0.5188964 Data Science Skills Year 3 Before
## 8 -0.8259560 Data Science Skills Year 3 Before
## 9 0.1918342 Data Science Skills Year 3 Before
## 10 1.6156635 Data Science Skills Year 3 Before
## 11 -2.8955512 Data Science Skills Year 3 Before
## 12 -0.2564358 Data Science Skills Year 3 Before
## 13 -2.8955512 Data Science Skills Year 3 Before
## 14 1.9571955 Data Science Skills Year 3 Before
## 15 0.3920065 Data Science Skills Year 3 Before
## 16 -0.5188964 Data Science Skills Year 3 Before
## 17 0.3920065 Data Science Skills Year 3 Before
## 18 0.3920065 Data Science Skills Year 3 Before# science identity post by year
ds_skills_post1 <- filter(techconfpost, Cohort == "Year 1")
ds_skills_post1## Ability Dimension Cohort Trial
## 1 3.4074384 Data Science Skills Year 1 After
## 2 4.8202632 Data Science Skills Year 1 After
## 3 2.7092572 Data Science Skills Year 1 After
## 4 1.9571955 Data Science Skills Year 1 After
## 5 0.3920064 Data Science Skills Year 1 After
## 6 3.1553096 Data Science Skills Year 1 After
## 7 2.1338550 Data Science Skills Year 1 After
## 8 4.0010730 Data Science Skills Year 1 After
## 9 2.5077364 Data Science Skills Year 1 After
## 10 4.3679136 Data Science Skills Year 1 After
## 11 2.9240041 Data Science Skills Year 1 After
## 12 2.7092572 Data Science Skills Year 1 After
## 13 0.7633858 Data Science Skills Year 1 After
## 14 2.5077364 Data Science Skills Year 1 After
## 15 2.9240041 Data Science Skills Year 1 After
## 16 1.7849850 Data Science Skills Year 1 Afterds_skills_post2 <- filter(techconfpost, Cohort == "Year 2")
ds_skills_post2## Ability Dimension Cohort Trial
## 1 4.367914 Data Science Skills Year 2 After
## 2 4.001073 Data Science Skills Year 2 After
## 3 1.784985 Data Science Skills Year 2 After
## 4 4.820263 Data Science Skills Year 2 After
## 5 2.709257 Data Science Skills Year 2 After
## 6 3.686306 Data Science Skills Year 2 After
## 7 5.446170 Data Science Skills Year 2 After
## 8 2.133855 Data Science Skills Year 2 After
## 9 2.316706 Data Science Skills Year 2 After
## 10 1.110964 Data Science Skills Year 2 After
## 11 3.407438 Data Science Skills Year 2 After
## 12 3.686306 Data Science Skills Year 2 Afterds_skills_post3 <- filter(techconfpost, Cohort == "Year 3")
ds_skills_post3## Ability Dimension Cohort Trial
## 1 1.957195 Data Science Skills Year 3 After
## 2 1.615663 Data Science Skills Year 3 After
## 3 2.924004 Data Science Skills Year 3 After
## 4 6.657984 Data Science Skills Year 3 After
## 5 1.615663 Data Science Skills Year 3 After
## 6 1.615663 Data Science Skills Year 3 After
## 7 1.110964 Data Science Skills Year 3 After
## 8 2.507736 Data Science Skills Year 3 After
## 9 3.407438 Data Science Skills Year 3 After
## 10 4.001073 Data Science Skills Year 3 After
## 11 4.367914 Data Science Skills Year 3 After
## 12 2.709257 Data Science Skills Year 3 After
## 13 2.507736 Data Science Skills Year 3 After
## 14 2.924004 Data Science Skills Year 3 After
## 15 4.367914 Data Science Skills Year 3 After
## 16 4.820263 Data Science Skills Year 3 After
## 17 2.133855 Data Science Skills Year 3 After
## 18 4.367914 Data Science Skills Year 3 After# t-testing science identity by year
# Year 1
t.test(ds_skills_post1$Ability, ds_skills_pre1$Ability)##
## Welch Two Sample t-test
##
## data: ds_skills_post1$Ability and ds_skills_pre1$Ability
## t = 5.6845, df = 29.684, p-value = 3.503e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.594562 3.384041
## sample estimates:
## mean of x mean of y
## 2.6915888 0.2022869cohensD(ds_skills_post1$Ability, ds_skills_pre1$Ability)## [1] 2.009757# Year 2
t.test(ds_skills_post2$Ability, ds_skills_pre2$Ability)##
## Welch Two Sample t-test
##
## data: ds_skills_post2$Ability and ds_skills_pre2$Ability
## t = 5.6842, df = 21.699, p-value = 1.077e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 2.040757 4.388293
## sample estimates:
## mean of x mean of y
## 3.289270 0.074745cohensD(ds_skills_post2$Ability, ds_skills_pre2$Ability)## [1] 2.320549# Year 3
t.test(ds_skills_post3$Ability, ds_skills_pre3$Ability)##
## Welch Two Sample t-test
##
## data: ds_skills_post3$Ability and ds_skills_pre3$Ability
## t = 6.558, df = 33.632, p-value = 1.725e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 2.28937 4.34663
## sample estimates:
## mean of x mean of y
## 3.0895691 -0.2284313cohensD(ds_skills_post3$Ability, ds_skills_pre3$Ability)## [1] 2.185985Python Skills
##
## Welch Two Sample t-test
##
## data: techcomfortpost$Ability and techcomfortpre$Ability
## t = 15.426, df = 89.051, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 4.968754 6.437964
## sample estimates:
## mean of x mean of y
## 6.641079 0.937720## [1] 3.216642Python Skills by Year
# science identity pre by year
python_pre1 <- filter(techcomfortpre, Cohort == "Year 1")
python_pre1## Ability Dimension Cohort Trial
## 1 1.6668819 Python Skills Year 1 Before
## 2 2.8461207 Python Skills Year 1 Before
## 3 -0.1996241 Python Skills Year 1 Before
## 4 -0.1996241 Python Skills Year 1 Before
## 5 1.0266831 Python Skills Year 1 Before
## 6 -0.1996241 Python Skills Year 1 Before
## 7 5.9798356 Python Skills Year 1 Before
## 8 1.6668819 Python Skills Year 1 Before
## 9 -0.1996241 Python Skills Year 1 Before
## 10 -0.1996241 Python Skills Year 1 Before
## 11 -0.1996241 Python Skills Year 1 Before
## 12 -0.1996241 Python Skills Year 1 Before
## 13 -0.1996241 Python Skills Year 1 Before
## 14 1.0266831 Python Skills Year 1 Before
## 15 -0.1996241 Python Skills Year 1 Before
## 16 -0.1996241 Python Skills Year 1 Beforepython_pre2 <- filter(techcomfortpre, Cohort == "Year 2")
python_pre2## Ability Dimension Cohort Trial
## 1 3.1439720 Python Skills Year 2 Before
## 2 4.1577919 Python Skills Year 2 Before
## 3 -0.1996241 Python Skills Year 2 Before
## 4 1.0266831 Python Skills Year 2 Before
## 5 -0.1996241 Python Skills Year 2 Before
## 6 -0.1996241 Python Skills Year 2 Before
## 7 -0.1996241 Python Skills Year 2 Before
## 8 -0.1996241 Python Skills Year 2 Before
## 9 4.6112586 Python Skills Year 2 Before
## 10 2.5150264 Python Skills Year 2 Before
## 11 -0.1996241 Python Skills Year 2 Before
## 12 -0.1996241 Python Skills Year 2 Beforepython_pre3 <- filter(techcomfortpre, Cohort == "Year 3")
python_pre3## Ability Dimension Cohort Trial
## 1 3.6764147 Python Skills Year 3 Before
## 2 -0.1996241 Python Skills Year 3 Before
## 3 -0.1996241 Python Skills Year 3 Before
## 4 6.5363310 Python Skills Year 3 Before
## 5 -0.1996241 Python Skills Year 3 Before
## 6 4.1577919 Python Skills Year 3 Before
## 7 -0.1996241 Python Skills Year 3 Before
## 8 -0.1996241 Python Skills Year 3 Before
## 9 -0.1996241 Python Skills Year 3 Before
## 10 -0.1996241 Python Skills Year 3 Before
## 11 -0.1996241 Python Skills Year 3 Before
## 12 -0.1996241 Python Skills Year 3 Before
## 13 -0.1996241 Python Skills Year 3 Before
## 14 3.4186043 Python Skills Year 3 Before
## 15 -0.1996241 Python Skills Year 3 Before
## 16 -0.1996241 Python Skills Year 3 Before
## 17 -0.1996241 Python Skills Year 3 Before
## 18 1.6668819 Python Skills Year 3 Before# science identity post by year
python_post1 <- filter(techcomfortpost, Cohort == "Year 1")
python_post1## Ability Dimension Cohort Trial
## 1 7.217052 Python Skills Year 1 After
## 2 7.812953 Python Skills Year 1 After
## 3 5.950066 Python Skills Year 1 After
## 4 6.141463 Python Skills Year 1 After
## 5 6.748451 Python Skills Year 1 After
## 6 5.950066 Python Skills Year 1 After
## 7 7.217052 Python Skills Year 1 After
## 8 4.580920 Python Skills Year 1 After
## 9 6.748451 Python Skills Year 1 After
## 10 6.141463 Python Skills Year 1 After
## 11 5.760429 Python Skills Year 1 After
## 12 4.366900 Python Skills Year 1 After
## 13 5.380136 Python Skills Year 1 After
## 14 5.760429 Python Skills Year 1 After
## 15 5.186832 Python Skills Year 1 After
## 16 5.760429 Python Skills Year 1 Afterpython_post2 <- filter(techcomfortpost, Cohort == "Year 2")
python_post2## Ability Dimension Cohort Trial
## 1 7.491356 Python Skills Year 2 After
## 2 9.952048 Python Skills Year 2 After
## 3 4.788244 Python Skills Year 2 After
## 4 7.491356 Python Skills Year 2 After
## 5 6.748451 Python Skills Year 2 After
## 6 6.336577 Python Skills Year 2 After
## 7 9.952048 Python Skills Year 2 After
## 8 5.186832 Python Skills Year 2 After
## 9 6.972738 Python Skills Year 2 After
## 10 4.788244 Python Skills Year 2 After
## 11 5.570921 Python Skills Year 2 After
## 12 7.491356 Python Skills Year 2 Afterpython_post3 <- filter(techcomfortpost, Cohort == "Year 3")
python_post3## Ability Dimension Cohort Trial
## 1 4.989882 Python Skills Year 3 After
## 2 5.186832 Python Skills Year 3 After
## 3 6.141463 Python Skills Year 3 After
## 4 9.952048 Python Skills Year 3 After
## 5 4.989882 Python Skills Year 3 After
## 6 5.380136 Python Skills Year 3 After
## 7 3.914069 Python Skills Year 3 After
## 8 6.141463 Python Skills Year 3 After
## 9 7.812953 Python Skills Year 3 After
## 10 9.952048 Python Skills Year 3 After
## 11 9.952048 Python Skills Year 3 After
## 12 7.812953 Python Skills Year 3 After
## 13 7.217052 Python Skills Year 3 After
## 14 7.491356 Python Skills Year 3 After
## 15 6.748451 Python Skills Year 3 After
## 16 9.952048 Python Skills Year 3 After
## 17 4.145073 Python Skills Year 3 After
## 18 8.216612 Python Skills Year 3 After# t-testing science identity by year
# Year 1
t.test(python_post1$Ability, python_pre1$Ability)##
## Welch Two Sample t-test
##
## data: python_post1$Ability and python_pre1$Ability
## t = 10.946, df = 23.525, p-value = 1.036e-10
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 4.284734 6.278547
## sample estimates:
## mean of x mean of y
## 6.0451932 0.7635529cohensD(python_post1$Ability, python_pre1$Ability)## [1] 3.870099# Year 2
t.test(python_post2$Ability, python_pre2$Ability)##
## Welch Two Sample t-test
##
## data: python_post2$Ability and python_pre2$Ability
## t = 7.6856, df = 21.854, p-value = 1.193e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 4.180347 7.271787
## sample estimates:
## mean of x mean of y
## 6.897514 1.171447cohensD(python_post2$Ability, python_pre2$Ability)## [1] 3.137618# Year 3
t.test(python_post3$Ability, python_pre3$Ability)##
## Welch Two Sample t-test
##
## data: python_post3$Ability and python_pre3$Ability
## t = 8.8424, df = 33.996, p-value = 2.471e-10
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 4.669601 7.456560
## sample estimates:
## mean of x mean of y
## 6.9997981 0.9367173cohensD(python_post3$Ability, python_pre3$Ability)## [1] 2.94747# Year 3 vs. 1 (PRE)
t.test(python_pre3$Ability, python_pre1$Ability)##
## Welch Two Sample t-test
##
## data: python_pre3$Ability and python_pre1$Ability
## t = 0.26877, df = 31.783, p-value = 0.7898
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.139565 1.485894
## sample estimates:
## mean of x mean of y
## 0.9367173 0.7635529t.test(python_pre2$Ability, python_pre1$Ability)##
## Welch Two Sample t-test
##
## data: python_pre2$Ability and python_pre1$Ability
## t = 0.59017, df = 22.169, p-value = 0.561
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.024813 1.840601
## sample estimates:
## mean of x mean of y
## 1.1714470 0.7635529cohensD(idpost3$Ability, idpre1$Ability)## [1] 0.2151985Growth across the five dimensions
Splitting by cohort
